Expert Modeling of Circulating Shaft Current for Fault Diagnosis of Large Alternating Machinery

Keywords

Abstract

Modern industrial plants have become so complex that the
technicians cannot be entrusted the task of fault detection
without serious risk of very costly failures.
Increased production level per plant, rigid production
schedules, high sensitivity services, and above all very
high capital investments have led to a change of attitude
and philosophy towards plant maintenance. If the
production process happens to be power generation then
the plant maintenance assumes an extraordinary
importance. In order to ensure maximum plant availability
and reliability it is necessary to have a properly planned
plant maintenance programme in conjunction with
production requirements. During the operation of large
rotating electrical machines, undesirable voltages occur
between the shaft ends and result in a circulating shaft
current through vital metallic parts like bearing, etc, in its
path, which may result in bearing damage. The most
important cause of asymmetries of magnetic circuit of an
alternating ring flux with the shaft are due to uneven air
gaps caused by displacement of the rotor etc… such
asymmetries are inevitable in large machines[5].
Reduction of the human experts involvement in the
diagnosis process has gradually taken place upon the
recent developments in the modern artificial intelligence
(AI) tools. Artificial neural networks (ANNs), fuzzy and
adaptive fuzzy systems, and expert systems are good
candidates for the automation of the diagnostic procedures
and e-maintenance application [1, 2, 3]. The present work
surveys
the principles and criteria of the diagnosis process and
introduces the current research achievements to apply
expert system techniques in the diagnostic systems of
electrical machines and drives. In this paper a new sensor
design is discussed and experimental results are presented
for an expert system application.